Without quality presence–absence data, discrimination metrics such as TSS can be misleading measures of model performance

作者：Boris Leroy, Robin Delsol, Bernard Hugueny, Christine N. Meynard, Chéïma Barhoumi, Morgane Barbet‐Massin, Céline Bellard · 发表于：Journal of Biogeography · 年份：2018 · DOI：10.1111/jbi.13402 · 被引用次数：425 · 研究领域：Species Distribution and Climate Change、Wildlife Ecology and Conservation、Genetic diversity and population structure

Abstract The discriminating capacity (i.e. ability to correctly classify presences and absences) of species distribution models ( SDM s) is commonly evaluated with metrics such as the area under the receiving operating characteristic curve ( AUC ), the Kappa statistic and the true skill statistic ( TSS ). AUC and Kappa have been repeatedly criticized, but TSS has fared relatively well since its introduction, mainly because it has been considered as independent of prevalence. In addition, discrimination metrics have been contested because they should be calculated on presence–absence data, but are often used on presence‐only or presence‐background data. Here, we investigate TSS and an alternative set of metrics—similarity indices, also known as F ‐measures. We first show that even in ideal conditions (i.e. perfectly random presence–absence sampling), TSS can be misleading because of its dependence on prevalence, whereas similarity/ F ‐measures provide adequate estimations of model discrimination capacity. Second, we show that in real‐world situations where sample prevalence is different from true species prevalence (i.e. biased sampling or presence‐pseudoabsence), no discrimination capacity metric provides adequate estimation of model discrimination capacity, including metrics specifically designed for modelling with presence‐pseudoabsence data. Our conclusions are twofold. First, they unequivocally impel SDM users to understand the potential shortcomings of discrimination met...